BayesSB

Using mathematical models to simulate and analyze biochemical
networks requires a principled approach to estimating unknown model
parameters and to discriminating between competing models. This problem
has been approached from four conceptually distinct ways (leaving aside
algorithmic specifics): (1) focusing on simple processes or small
reaction networks for which identifiable models can be constructed (2)
for non-identifiable models, using a single set of best-fit parameter
values is often used, ignoring the lack of certainty about parameters
(3) partly mitigating non-identifiability by using families of a few
hundred fits under the assumption that properties that are invariant
across sets of parameters are of the greatest (4) applying rigorous
sampling methods to recover the complete probability distribution of
parameters, accounting for both experimental error and model
non-identifiability, and then using the distribution in model-based
prediction or model discrimination. BayesSB implements the fourth
approach.

BayesSB is an algorithm and Python software package for estimating
parameter distributions in reaction models of cellular biochemistry and
for discriminating between models having different numbers of unknown
parameters. The algorithm is described in detail in Eydgahi et al.
Properties of cell death models calibrated and compared using Bayesian
approaches. Mol Syst Biol (in review).